Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation
arXiv:2605.22765v1 Announce Type: new Abstract: Discrete diffusion models are often trained through clean-data prediction, but the prediction can be used in different ways to define the reverse dynamics. In Masked Diffusion Models (MDM) these choices largely coincide, whereas in Uniform Diffusion Models (UDM) they do not. We show that the standard plug-in bridge parameterization for UDM is not optimized by the denoising posterior, but by a leave-one-out posterior that predicts each clean token without using its own noisy observation. This identifies a mismatch between the plug-in ELBO and the
This paper refines the understanding of Uniform Diffusion Models, a recent development in AI, indicating ongoing advancements and technical corrections within the field.
For researchers and practitioners in AI, this represents a technical correction and optimization in the foundational models for generative AI, potentially leading to more efficient or effective model training.
The understanding of optimal denoiser parameterization for Uniform Diffusion Models (UDM) is changed, moving from a standard plug-in bridge to a leave-one-out posterior.
- · AI Researchers
- · Generative AI Model Developers
- · Developers using suboptimal UDM implementations
Improved theoretical understanding and practical implementation of Uniform Diffusion Models for generative AI.
Potentially more robust or higher-quality outputs from generative AI models utilizing corrected UDM approaches.
Accelerated development in niche applications of generative AI where UDM's strengths are particularly relevant due to enhanced model performance.
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